Many process facilities (e.g., a manufacturing plant, a mineral or crude oil refinery, etc.) are managed using distributed control systems. Typical contemporary control systems include numerous modules tailored to monitor and/or control various processes of the facility. Conventional means link these modules together to produce the distributed nature of the control system. This affords increased performance and a capability to expand or reduce the control system to satisfy changing facility needs.
Process facility management providers, such as Honeywell, Inc., develop control systems that can be tailored to satisfy wide ranges of process requirements (e.g., global, local or otherwise) and facility types (e.g., manufacturing, warehousing, refining, etc.). Such providers have two principal objectives. The first objective is to centralize control of as many processes as possible to improve an overall efficiency of the facility. The second objective is to support a common interface that communicates data among various modules controlling or monitoring the processes, and also with any such centralized controller or operator center.
Each process, or group of associated processes, has one or more input characteristics (e.g., flow, feed, power, etc.) and one or more output characteristics (e.g., temperature, pressure, etc.) associated with it. Model predictive control ("MPC") techniques have been used to optimize certain processes as a function of such characteristics. One MPC technique uses algorithmic representations of certain processes to estimate characteristic values (represented as parameters, variables, etc.) associated with them that can be used to better control such processes. In recent years, physical, economic and other factors have been incorporated into control systems for these associated processes.
Examples of such techniques are described in U.S. Pat. No. 5,351,184 entitled "Method of Multivariable Predictive Control Utilizing Range Control;" U.S. Pat. No. 5,561,599 entitled "Method of Incorporating Independent Feedforward Control in a Multivariable Predictive Controller;" U.S. Pat. No. 5,572,420 entitled "Method of Optimal Controller Design of Multivariable Predictive Control Utilizing Range Control;" and U.S. Pat. No. 5,574,638 entitled "Method of Optimal Scaling of Variables in a Multivariable Predictive Controller Utilizing Range Control," all of which are commonly owned by the assignee of the present invention and incorporated herein by reference for all purposes (the foregoing issued patents and U.S. patent application Ser. Nos. 08/916,870 and 08/920,265, previously incorporated herein by reference, are collectively referred to hereafter as the "Honeywell Patents and Application").
The distributed control systems used to monitor and control a process are frequently linked by common communication pathways, such as by a local area network (LAN) architecture or by a wide area network (WAN) architecture. When a requesting node needs a datum from a responding node, it issues a request for the datum across the network and the responding node then returns the datum back across the network. Many process control systems use a supervisory control LAN or WAN integrated with one or more process control networks. The process control networks contain the basic raw data required by the supervisory control network and other process control networks.
An important function in distributed control systems is the generation and distribution of notifications, also known as events. A notification is an indication of some abnormal or exceptional situation relating to a controlled process or its measurement and control equipment. A process controller generates notifications that are distributed to a notification client, which is an end-point application that requires the notifications. For example, notifications may comprise alarms, system events, operator messages, and the like, that are related to user-visible process, equipment and hardware exceptions.
For example, a first process controller that requires process data is a notification client with respect to a second process controller that contains that process data. In the case of any abnormality, such as a communication loss by the second process controller, the second process controller may be required to generate notifications when the abnormality is removed. Typically, the first process controller becomes aware that the second process controller has recovered and requests a notification recovery from the second process controller. The second process controller then regenerates all notifications that may have occurred during the communications failure and transmits them to the first process controller. This type of notification distribution system has drawbacks, however. The system is dependent upon the notification client (i.e., the first process controller) requesting the notification recovery. This may not occur for some time after the abnormality has ended and the second process controller has recovered. Additionally, the process controller that is generating the notifications may have many notification clients. If each notification client separately requests and receives a notification recovery from the notification generating process controller, a large amount of network traffic is generated, thereby reducing overall system capacity.
There is therefore a need in the art for improved process control systems capable of generating and distributing notifications immediately upon recovery of a process controller, without the need for a notification recovery request by a notification client. There is a further need for improved process control systems capable of distributing notifications rapidly from one network node to a plurality of notification clients.